Overview

Dataset statistics

Number of variables13
Number of observations9841
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Text2
Numeric11

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Murder is highly overall correlated with RapeHigh correlation
Rape is highly overall correlated with Murder and 2 other fieldsHigh correlation
Hurt is highly overall correlated with Rape and 1 other fieldsHigh correlation
Other Crimes Against STs is highly overall correlated with Rape and 1 other fieldsHigh correlation
Rape is highly skewed (γ1 = 20.05760033)Skewed
Dacoity is highly skewed (γ1 = 27.8530928)Skewed
Robbery is highly skewed (γ1 = 22.7508024)Skewed
Arson is highly skewed (γ1 = 32.6223407)Skewed
Protection of Civil Rights (PCR) Act is highly skewed (γ1 = 65.79569543)Skewed
Murder has 8564 (87.0%) zerosZeros
Rape has 7308 (74.3%) zerosZeros
Kidnapping Abduction has 8966 (91.1%) zerosZeros
Dacoity has 9707 (98.6%) zerosZeros
Robbery has 9587 (97.4%) zerosZeros
Arson has 9353 (95.0%) zerosZeros
Hurt has 7542 (76.6%) zerosZeros
Protection of Civil Rights (PCR) Act has 9707 (98.6%) zerosZeros
Prevention of atrocities (POA) Act has 7602 (77.2%) zerosZeros
Other Crimes Against STs has 6477 (65.8%) zerosZeros

Reproduction

Analysis started2023-09-12 19:13:18.739967
Analysis finished2023-09-12 19:14:50.953838
Duration1 minute and 32.21 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
2023-09-13T00:44:52.329623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length13
Mean length9.8073367
Min length3

Characters and Unicode

Total characters96514
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANDHRA PRADESH
2nd rowANDHRA PRADESH
3rd rowANDHRA PRADESH
4th rowANDHRA PRADESH
5th rowANDHRA PRADESH
ValueCountFrequency (%)
pradesh 2448
 
17.3%
uttar 955
 
6.8%
madhya 683
 
4.8%
maharashtra 598
 
4.2%
bihar 585
 
4.1%
tamil 509
 
3.6%
nadu 509
 
3.6%
rajasthan 498
 
3.5%
odisha 467
 
3.3%
453
 
3.2%
Other values (37) 6423
45.5%
2023-09-13T00:44:54.530736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 20217
20.9%
H 8947
 
9.3%
R 8637
 
8.9%
S 5465
 
5.7%
T 5325
 
5.5%
D 5184
 
5.4%
4287
 
4.4%
N 3875
 
4.0%
M 3824
 
4.0%
E 3480
 
3.6%
Other values (38) 27273
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85233
88.3%
Lowercase Letter 6535
 
6.8%
Space Separator 4287
 
4.4%
Other Punctuation 459
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 20217
23.7%
H 8947
10.5%
R 8637
10.1%
S 5465
 
6.4%
T 5325
 
6.2%
D 5184
 
6.1%
N 3875
 
4.5%
M 3824
 
4.5%
E 3480
 
4.1%
U 3131
 
3.7%
Other values (13) 17148
20.1%
Lowercase Letter
ValueCountFrequency (%)
a 1737
26.6%
h 769
11.8%
r 736
11.3%
s 496
 
7.6%
d 431
 
6.6%
t 431
 
6.6%
e 322
 
4.9%
n 290
 
4.4%
i 266
 
4.1%
m 202
 
3.1%
Other values (13) 855
13.1%
Space Separator
ValueCountFrequency (%)
4287
100.0%
Other Punctuation
ValueCountFrequency (%)
& 459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91768
95.1%
Common 4746
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 20217
22.0%
H 8947
 
9.7%
R 8637
 
9.4%
S 5465
 
6.0%
T 5325
 
5.8%
D 5184
 
5.6%
N 3875
 
4.2%
M 3824
 
4.2%
E 3480
 
3.8%
U 3131
 
3.4%
Other values (36) 23683
25.8%
Common
ValueCountFrequency (%)
4287
90.3%
& 459
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 20217
20.9%
H 8947
 
9.3%
R 8637
 
8.9%
S 5465
 
5.7%
T 5325
 
5.5%
D 5184
 
5.4%
4287
 
4.4%
N 3875
 
4.0%
M 3824
 
4.0%
E 3480
 
3.6%
Other values (38) 27273
28.3%
Distinct833
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
2023-09-13T00:44:55.855753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.3826847
Min length3

Characters and Unicode

Total characters82494
Distinct characters38
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.4%

Sample

1st rowADILABAD
2nd rowANANTAPUR
3rd rowCHITTOOR
4th rowCUDDAPAH
5th rowEAST GODAVARI
ValueCountFrequency (%)
total 456
 
3.9%
rural 343
 
2.9%
commr 229
 
1.9%
rly 221
 
1.9%
west 120
 
1.0%
g.r.p 115
 
1.0%
east 111
 
0.9%
nagar 99
 
0.8%
south 91
 
0.8%
north 91
 
0.8%
Other values (760) 9956
84.1%
2023-09-13T00:44:57.927736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15586
18.9%
R 8434
 
10.2%
I 4672
 
5.7%
N 4647
 
5.6%
H 4593
 
5.6%
U 4478
 
5.4%
L 3868
 
4.7%
T 3679
 
4.5%
O 3228
 
3.9%
D 3087
 
3.7%
Other values (28) 26222
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79192
96.0%
Space Separator 1991
 
2.4%
Other Punctuation 1022
 
1.2%
Dash Punctuation 84
 
0.1%
Close Punctuation 56
 
0.1%
Open Punctuation 56
 
0.1%
Decimal Number 52
 
0.1%
Lowercase Letter 39
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15586
19.7%
R 8434
 
10.7%
I 4672
 
5.9%
N 4647
 
5.9%
H 4593
 
5.8%
U 4478
 
5.7%
L 3868
 
4.9%
T 3679
 
4.6%
O 3228
 
4.1%
D 3087
 
3.9%
Other values (15) 22920
28.9%
Other Punctuation
ValueCountFrequency (%)
. 1009
98.7%
/ 11
 
1.1%
& 2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
a 13
33.3%
n 13
33.3%
d 13
33.3%
Decimal Number
ValueCountFrequency (%)
2 26
50.0%
4 26
50.0%
Space Separator
ValueCountFrequency (%)
1991
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79231
96.0%
Common 3263
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15586
19.7%
R 8434
 
10.6%
I 4672
 
5.9%
N 4647
 
5.9%
H 4593
 
5.8%
U 4478
 
5.7%
L 3868
 
4.9%
T 3679
 
4.6%
O 3228
 
4.1%
D 3087
 
3.9%
Other values (18) 22959
29.0%
Common
ValueCountFrequency (%)
1991
61.0%
. 1009
30.9%
- 84
 
2.6%
) 56
 
1.7%
( 56
 
1.7%
2 26
 
0.8%
4 26
 
0.8%
/ 11
 
0.3%
_ 2
 
0.1%
& 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15586
18.9%
R 8434
 
10.2%
I 4672
 
5.7%
N 4647
 
5.6%
H 4593
 
5.6%
U 4478
 
5.4%
L 3868
 
4.7%
T 3679
 
4.5%
O 3228
 
3.9%
D 3087
 
3.7%
Other values (28) 26222
31.8%

Year
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.1617
Minimum2001
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:44:58.501737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12004
median2007
Q32010
95-th percentile2013
Maximum2013
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7554538
Coefficient of variation (CV)0.0018710271
Kurtosis-1.2202554
Mean2007.1617
Median Absolute Deviation (MAD)3
Skewness-0.051953776
Sum19752478
Variance14.103433
MonotonicityIncreasing
2023-09-13T00:44:59.082737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2013 823
 
8.4%
2012 811
 
8.2%
2011 791
 
8.0%
2010 779
 
7.9%
2009 767
 
7.8%
2008 761
 
7.7%
2007 743
 
7.6%
2006 740
 
7.5%
2005 734
 
7.5%
2004 729
 
7.4%
Other values (3) 2163
22.0%
ValueCountFrequency (%)
2001 716
7.3%
2002 719
7.3%
2003 728
7.4%
2004 729
7.4%
2005 734
7.5%
2006 740
7.5%
2007 743
7.6%
2008 761
7.7%
2009 767
7.8%
2010 779
7.9%
ValueCountFrequency (%)
2013 823
8.4%
2012 811
8.2%
2011 791
8.0%
2010 779
7.9%
2009 767
7.8%
2008 761
7.7%
2007 743
7.6%
2006 740
7.5%
2005 734
7.5%
2004 729
7.4%

Murder
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40382075
Minimum0
Maximum62
Zeros8564
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:44:59.723741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4110437
Coefficient of variation (CV)5.970579
Kurtosis232.45067
Mean0.40382075
Median Absolute Deviation (MAD)0
Skewness13.613787
Sum3974
Variance5.8131317
MonotonicityNot monotonic
2023-09-13T00:45:00.387759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 8564
87.0%
1 738
 
7.5%
2 232
 
2.4%
3 102
 
1.0%
4 44
 
0.4%
5 30
 
0.3%
6 22
 
0.2%
8 12
 
0.1%
10 11
 
0.1%
11 9
 
0.1%
Other values (30) 77
 
0.8%
ValueCountFrequency (%)
0 8564
87.0%
1 738
 
7.5%
2 232
 
2.4%
3 102
 
1.0%
4 44
 
0.4%
5 30
 
0.3%
6 22
 
0.2%
7 6
 
0.1%
8 12
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
62 1
< 0.1%
49 2
< 0.1%
48 1
< 0.1%
47 2
< 0.1%
46 2
< 0.1%
45 2
< 0.1%
43 1
< 0.1%
42 1
< 0.1%
41 1
< 0.1%
38 1
< 0.1%

Rape
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7101921
Minimum0
Maximum329
Zeros7308
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:01.264737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7
Maximum329
Range329
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.695473
Coefficient of variation (CV)6.8386899
Kurtosis471.58098
Mean1.7101921
Median Absolute Deviation (MAD)0
Skewness20.0576
Sum16830
Variance136.78409
MonotonicityNot monotonic
2023-09-13T00:45:02.041767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7308
74.3%
1 879
 
8.9%
2 438
 
4.5%
3 278
 
2.8%
4 199
 
2.0%
5 140
 
1.4%
6 98
 
1.0%
7 95
 
1.0%
8 64
 
0.7%
9 54
 
0.5%
Other values (69) 288
 
2.9%
ValueCountFrequency (%)
0 7308
74.3%
1 879
 
8.9%
2 438
 
4.5%
3 278
 
2.8%
4 199
 
2.0%
5 140
 
1.4%
6 98
 
1.0%
7 95
 
1.0%
8 64
 
0.7%
9 54
 
0.5%
ValueCountFrequency (%)
329 1
< 0.1%
312 1
< 0.1%
308 1
< 0.1%
306 1
< 0.1%
294 1
< 0.1%
288 2
< 0.1%
284 1
< 0.1%
270 1
< 0.1%
263 2
< 0.1%
238 1
< 0.1%

Kidnapping Abduction
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2286353
Minimum0
Maximum46
Zeros8966
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:03.383651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum46
Range46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4360901
Coefficient of variation (CV)6.2811389
Kurtosis357.51613
Mean0.2286353
Median Absolute Deviation (MAD)0
Skewness15.823641
Sum2250
Variance2.0623547
MonotonicityNot monotonic
2023-09-13T00:45:04.816109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 8966
91.1%
1 534
 
5.4%
2 155
 
1.6%
3 49
 
0.5%
4 32
 
0.3%
6 18
 
0.2%
5 17
 
0.2%
7 15
 
0.2%
8 9
 
0.1%
9 9
 
0.1%
Other values (17) 37
 
0.4%
ValueCountFrequency (%)
0 8966
91.1%
1 534
 
5.4%
2 155
 
1.6%
3 49
 
0.5%
4 32
 
0.3%
5 17
 
0.2%
6 18
 
0.2%
7 15
 
0.2%
8 9
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
46 1
< 0.1%
45 1
< 0.1%
39 1
< 0.1%
30 2
< 0.1%
27 1
< 0.1%
24 1
< 0.1%
22 2
< 0.1%
21 2
< 0.1%
20 1
< 0.1%
18 1
< 0.1%

Dacoity
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043491515
Minimum0
Maximum29
Zeros9707
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:05.985106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.66315531
Coefficient of variation (CV)15.247924
Kurtosis971.09606
Mean0.043491515
Median Absolute Deviation (MAD)0
Skewness27.853093
Sum428
Variance0.43977496
MonotonicityNot monotonic
2023-09-13T00:45:07.389917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9707
98.6%
1 79
 
0.8%
2 14
 
0.1%
3 10
 
0.1%
4 6
 
0.1%
5 6
 
0.1%
6 5
 
0.1%
12 2
 
< 0.1%
17 2
 
< 0.1%
13 2
 
< 0.1%
Other values (5) 8
 
0.1%
ValueCountFrequency (%)
0 9707
98.6%
1 79
 
0.8%
2 14
 
0.1%
3 10
 
0.1%
4 6
 
0.1%
5 6
 
0.1%
6 5
 
0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
29 2
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
15 1
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
9 2
 
< 0.1%
7 2
 
< 0.1%
6 5
0.1%
5 6
0.1%

Robbery
Real number (ℝ)

SKEWED  ZEROS 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082308708
Minimum0
Maximum34
Zeros9587
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:09.063833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0349252
Coefficient of variation (CV)12.573702
Kurtosis601.19119
Mean0.082308708
Median Absolute Deviation (MAD)0
Skewness22.750802
Sum810
Variance1.0710701
MonotonicityNot monotonic
2023-09-13T00:45:10.684459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9587
97.4%
1 153
 
1.6%
2 43
 
0.4%
3 15
 
0.2%
4 10
 
0.1%
5 7
 
0.1%
14 3
 
< 0.1%
10 2
 
< 0.1%
16 2
 
< 0.1%
6 2
 
< 0.1%
Other values (15) 17
 
0.2%
ValueCountFrequency (%)
0 9587
97.4%
1 153
 
1.6%
2 43
 
0.4%
3 15
 
0.2%
4 10
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
34 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
28 1
< 0.1%
26 1
< 0.1%
24 1
< 0.1%
23 1
< 0.1%
20 1
< 0.1%
18 2
< 0.1%

Arson
Real number (ℝ)

SKEWED  ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1146225
Minimum0
Maximum64
Zeros9353
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:12.325030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0559888
Coefficient of variation (CV)9.2127535
Kurtosis1602.7615
Mean0.1146225
Median Absolute Deviation (MAD)0
Skewness32.622341
Sum1128
Variance1.1151124
MonotonicityNot monotonic
2023-09-13T00:45:13.759744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9353
95.0%
1 313
 
3.2%
2 87
 
0.9%
3 23
 
0.2%
5 14
 
0.1%
4 13
 
0.1%
7 8
 
0.1%
8 7
 
0.1%
6 6
 
0.1%
11 3
 
< 0.1%
Other values (8) 14
 
0.1%
ValueCountFrequency (%)
0 9353
95.0%
1 313
 
3.2%
2 87
 
0.9%
3 23
 
0.2%
4 13
 
0.1%
5 14
 
0.1%
6 6
 
0.1%
7 8
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
64 1
 
< 0.1%
32 2
 
< 0.1%
28 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 3
< 0.1%
10 3
< 0.1%
9 3
< 0.1%
8 7
0.1%

Hurt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1379941
Minimum0
Maximum329
Zeros7542
Zeros (%)76.6%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:15.619911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum329
Range329
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.041622
Coefficient of variation (CV)5.6322056
Kurtosis245.27078
Mean2.1379941
Median Absolute Deviation (MAD)0
Skewness13.835103
Sum21040
Variance145.00067
MonotonicityNot monotonic
2023-09-13T00:45:17.503950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7542
76.6%
1 637
 
6.5%
2 344
 
3.5%
3 237
 
2.4%
4 163
 
1.7%
5 151
 
1.5%
7 109
 
1.1%
6 105
 
1.1%
8 64
 
0.7%
9 54
 
0.5%
Other values (89) 435
 
4.4%
ValueCountFrequency (%)
0 7542
76.6%
1 637
 
6.5%
2 344
 
3.5%
3 237
 
2.4%
4 163
 
1.7%
5 151
 
1.5%
6 105
 
1.1%
7 109
 
1.1%
8 64
 
0.7%
9 54
 
0.5%
ValueCountFrequency (%)
329 1
< 0.1%
290 1
< 0.1%
263 1
< 0.1%
247 1
< 0.1%
245 1
< 0.1%
235 1
< 0.1%
216 1
< 0.1%
207 1
< 0.1%
204 1
< 0.1%
199 1
< 0.1%

Protection of Civil Rights (PCR) Act
Real number (ℝ)

SKEWED  ZEROS 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084544254
Minimum0
Maximum160
Zeros9707
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:19.412698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum160
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8792745
Coefficient of variation (CV)22.228294
Kurtosis5375.9994
Mean0.084544254
Median Absolute Deviation (MAD)0
Skewness65.795695
Sum832
Variance3.5316727
MonotonicityNot monotonic
2023-09-13T00:45:20.652106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9707
98.6%
1 48
 
0.5%
2 16
 
0.2%
4 14
 
0.1%
3 14
 
0.1%
5 10
 
0.1%
10 5
 
0.1%
8 5
 
0.1%
6 4
 
< 0.1%
14 2
 
< 0.1%
Other values (15) 16
 
0.2%
ValueCountFrequency (%)
0 9707
98.6%
1 48
 
0.5%
2 16
 
0.2%
3 14
 
0.1%
4 14
 
0.1%
5 10
 
0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 5
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
160 1
< 0.1%
39 1
< 0.1%
34 1
< 0.1%
26 1
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
21 2
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
17 1
< 0.1%

Prevention of atrocities (POA) Act
Real number (ℝ)

ZEROS 

Distinct131
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2917386
Minimum0
Maximum667
Zeros7602
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:22.310499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum667
Range667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.569067
Coefficient of variation (CV)6.2486939
Kurtosis396.89585
Mean3.2917386
Median Absolute Deviation (MAD)0
Skewness16.894961
Sum32394
Variance423.08653
MonotonicityNot monotonic
2023-09-13T00:45:24.031917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7602
77.2%
1 540
 
5.5%
2 334
 
3.4%
3 190
 
1.9%
4 147
 
1.5%
5 95
 
1.0%
6 93
 
0.9%
7 66
 
0.7%
8 64
 
0.7%
9 53
 
0.5%
Other values (121) 657
 
6.7%
ValueCountFrequency (%)
0 7602
77.2%
1 540
 
5.5%
2 334
 
3.4%
3 190
 
1.9%
4 147
 
1.5%
5 95
 
1.0%
6 93
 
0.9%
7 66
 
0.7%
8 64
 
0.7%
9 53
 
0.5%
ValueCountFrequency (%)
667 1
< 0.1%
665 1
< 0.1%
579 1
< 0.1%
513 1
< 0.1%
406 1
< 0.1%
355 1
< 0.1%
326 1
< 0.1%
310 1
< 0.1%
303 2
< 0.1%
298 1
< 0.1%

Other Crimes Against STs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4140839
Minimum0
Maximum1509
Zeros6477
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-13T00:45:25.964678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile27
Maximum1509
Range1509
Interquartile range (IQR)3

Descriptive statistics

Standard deviation47.325425
Coefficient of variation (CV)6.3831791
Kurtosis381.22956
Mean7.4140839
Median Absolute Deviation (MAD)0
Skewness17.562716
Sum72962
Variance2239.6959
MonotonicityNot monotonic
2023-09-13T00:45:27.982836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6477
65.8%
1 536
 
5.4%
2 351
 
3.6%
3 260
 
2.6%
4 188
 
1.9%
5 167
 
1.7%
6 160
 
1.6%
7 146
 
1.5%
8 121
 
1.2%
9 95
 
1.0%
Other values (163) 1340
 
13.6%
ValueCountFrequency (%)
0 6477
65.8%
1 536
 
5.4%
2 351
 
3.6%
3 260
 
2.6%
4 188
 
1.9%
5 167
 
1.7%
6 160
 
1.6%
7 146
 
1.5%
8 121
 
1.2%
9 95
 
1.0%
ValueCountFrequency (%)
1509 1
< 0.1%
1375 1
< 0.1%
1111 1
< 0.1%
1077 1
< 0.1%
1040 1
< 0.1%
980 1
< 0.1%
967 1
< 0.1%
891 1
< 0.1%
867 1
< 0.1%
819 1
< 0.1%

Interactions

2023-09-13T00:44:40.647784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:23.329106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:31.005630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:37.575711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:44.276679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:50.972677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:00.528690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:07.775681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:16.459798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:23.927792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:32.360795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:41.338789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:24.870618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:31.568703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:38.185682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:44.820677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:51.570686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:01.239680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:08.341702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:17.492788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:24.577797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:33.170787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:41.982795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:25.422615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:32.168700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:38.777673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:45.440701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:52.210683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:01.951680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:09.323023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:18.187804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:25.173798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:33.998798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:42.605787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:25.964616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:32.730707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:39.364682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:45.996683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:52.927680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:02.587702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:10.351674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:18.800791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:25.759791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:34.705781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:43.191791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:26.469600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:33.249685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:39.877680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:46.898690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:53.763695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:03.169684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:11.915640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:19.406786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:26.348790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:35.266798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:43.820796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:27.383616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:33.825684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:40.509694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:47.484688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:54.880674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:03.845690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:12.830132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:20.069807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:27.138787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:35.881796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:44.510787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:27.942614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:34.457683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:41.149676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:48.045704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:55.811681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:04.464683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:13.483112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:20.726799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:27.989784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:36.655781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:45.131780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:28.475614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:35.055701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:41.744684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:48.600673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:57.048677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:05.118689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:14.048726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:21.361787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:28.745786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:37.415795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:45.756784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:29.023619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:35.677688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:42.341683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:49.132683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:58.079675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:05.736683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:14.627784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:21.989800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:29.522800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:38.220795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:46.423789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:29.611598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:36.330681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:43.007686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:49.751681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:58.935673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:06.476675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:15.255799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:22.668791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:30.419783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:38.910805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:47.144785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:30.372606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:36.968678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:43.644684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:50.366695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:43:59.702681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:07.116681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:15.860780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:23.336790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:31.436779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-13T00:44:39.778788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-13T00:45:29.477623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearMurderRapeKidnapping AbductionDacoityRobberyArsonHurtProtection of Civil Rights (PCR) ActPrevention of atrocities (POA) ActOther Crimes Against STs
Year1.000-0.0210.0170.013-0.026-0.047-0.0430.005-0.0570.0020.011
Murder-0.0211.0000.5260.3900.2120.2790.3070.4630.1070.2690.496
Rape0.0170.5261.0000.4280.1630.2300.3140.5840.1090.3470.665
Kidnapping Abduction0.0130.3900.4281.0000.2210.3000.2580.4070.1150.1970.384
Dacoity-0.0260.2120.1630.2211.0000.3790.1830.1870.0390.0980.153
Robbery-0.0470.2790.2300.3000.3791.0000.2550.2670.0530.1430.214
Arson-0.0430.3070.3140.2580.1830.2551.0000.3180.1040.2030.299
Hurt0.0050.4630.5840.4070.1870.2670.3181.0000.1190.3720.594
Protection of Civil Rights (PCR) Act-0.0570.1070.1090.1150.0390.0530.1040.1191.0000.1680.121
Prevention of atrocities (POA) Act0.0020.2690.3470.1970.0980.1430.2030.3720.1681.0000.390
Other Crimes Against STs0.0110.4960.6650.3840.1530.2140.2990.5940.1210.3901.000

Missing values

2023-09-13T00:44:48.453797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-13T00:44:49.876627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

STATE/UTDISTRICTYearMurderRapeKidnapping AbductionDacoityRobberyArsonHurtProtection of Civil Rights (PCR) ActPrevention of atrocities (POA) ActOther Crimes Against STs
0ANDHRA PRADESHADILABAD200101200020013
1ANDHRA PRADESHANANTAPUR20010000007016
2ANDHRA PRADESHCHITTOOR20010000002000
3ANDHRA PRADESHCUDDAPAH20010000002020
4ANDHRA PRADESHEAST GODAVARI200100000000014
5ANDHRA PRADESHGUNTAKAL RLY.20010000000000
6ANDHRA PRADESHGUNTUR2001021000100319
7ANDHRA PRADESHHYDERABAD CITY20010000000002
8ANDHRA PRADESHKARIMNAGAR200100000015002
9ANDHRA PRADESHKHAMMAM200114000080920
STATE/UTDISTRICTYearMurderRapeKidnapping AbductionDacoityRobberyArsonHurtProtection of Civil Rights (PCR) ActPrevention of atrocities (POA) ActOther Crimes Against STs
813Delhi UTSOUTH-EAST20130000000000
814Delhi UTSOUTH-WEST20130000000000
815Delhi UTSTF20130000000000
816Delhi UTWEST20130000000000
817Delhi UTZZ TOTAL20130000000000
818LakshadweepLAKSHADWEEP20130000000000
819LakshadweepZZ TOTAL20130000000000
820PuducherryKARAIKAL20130000000000
821PuducherryPUDUCHERRY20130000000000
822PuducherryZZ TOTAL20130000000000

Duplicate rows

Most frequently occurring

STATE/UTDISTRICTYearMurderRapeKidnapping AbductionDacoityRobberyArsonHurtProtection of Civil Rights (PCR) ActPrevention of atrocities (POA) ActOther Crimes Against STs# duplicates
0JAMMU & KASHMIRANANTNAG200100000000002
1NAGALANDTOTAL200500000000002